Pseudo random sequences based on elliptic curve subgroups and mathematical model for its application to digital image security

Multimedia Tools and Applications - The security strength of the elliptic curve cryptosystems (ECC) is due to its core operations-based group law. This aspect of the elliptic curve provides key...     

Adaptive reallocation of cybersecurity analysts to sensors for balancing risk between sensors

Cyber Security Operations Center (CSOC) is a service-oriented system. Analysts work in shifts, and the goal at the end of each shift is to ensure that all alerts from each sensor (client) are analyzed. The goal is often not met because the CSOC is faced with adverse conditions such as variations in alert generation rates or in the time taken to thoroughly analyze new alerts. Current practice at many CSOCs is to pre-assign analysts to sensors based on their expertise, and the alerts from the sensors are triaged, queued, and presented to analysts. Under adverse conditions, some sensors have more number of unanalyzed alerts (backlogs) than others, which results in a major security gap for the clients if left unattended. Hence, there is a need to dynamically reallocate analysts to sensors; however, there does not exist a mechanism to ensure the following objectives: (i) balancing the number of unanalyzed alerts among sensors while maximizing the number of alerts investigated by optimally reallocating analysts to sensors in a shift, (ii) ensuring desirable properties of the CSOC: minimizing the disruption to the analyst to sensor allocation made at the beginning of the shift when analysts report to work, balancing of workload among analysts, and maximizing analyst utilization. The paper presents a technical solution to achieve the objectives and answers two important research questions: (i) detection of triggers, which determines when-to reallocate, and (ii) how to optimally reallocate analysts to sensors, which enable a CSOC manager to effectively use reallocation as a decision-making tool.     

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.

     

Effectiveness of crypto-transcoding for H.264/AVC and HEVC video bit-streams

Multimedia Tools and Applications - To avoid delays arising from a need to decrypt a video prior to transcoding and then re-encrypt it afterwards, this paper assesses a selective encryption (SE)...     

Effect of annealing and oxygenation on the DC conductivity of amorphous germanium

Thin films of amorphous germanium were deposited in an oxygen atmosphere.dc conductivity results are interpreted considering the possibility of the formation of Ge-O bonds. The density of states was determined. Results of conductivity are interpreted using the Davis-Mott model. Change in conductivity in annealed films of V-a-Ge and O-a-Ge is also reported.     

Prediction of polyethylene melt rheological properties from molecular weight distribution data obtained by gel permeation chromatography

A method is presented for predicting rheological characteristics, such as shear dependent (non-Newtonian) viscosity and components of linear oscillatory (complex) response functions for polyethylene melts from molecular weight distribution data obtained from gel permeation chromatographic (GPC) analysis. The results are compared with measured values of the rheological functions obtained from a variety of instruments over an extensive range of shear rates-and frequencies. The agreement between predicted and measured rheological functions is quite good for high density resins. However, for a low density resin the agreement is not as good, although still reasonable over a considerable range of conditions. It is concluded that the quality of the GPC data is the key factor in the degree of success of the method.     

Blown films of nanocomposites prepared from low density polyethylene and a sodium ionomer of poly(ethylene-co-methacrylic acid)

A detailed study of the performance of blown films prepared from nanocomposites based on LDPE and a sodium ionomer of poly(ethylene-co-methacrylic acid) is reported. The organoclay content and film blowing conditions were varied to determine the effect of platelet concentration, exfoliation and orientation on film properties. Mechanical properties including stiffness, puncture resistance, and resistance to tear propagation were evaluated and compared to corresponding properties of unfilled polymer films. Permeability of the films to moisture and common atmospheric gases like oxygen, nitrogen, and carbon dioxide was also measured using standard testing methods.In general, films prepared from nanocomposites based on the ionomer exhibited greater improvements in mechanical and barrier properties over unfilled polymer compared to similar films prepared from nanocomposites based on LDPE. This is due to the greater degree of organoclay exfoliation achieved in the ionomer compared to LDPE. The addition of 3 wt% MMT to the ionomer increased the tensile modulus of blown films by an average of 50% without sacrificing much tear strength, puncture resistance or film extensibility. Gas permeability in these films was lowered by 40% and moisture transmission rate was reduced by 60%.